| Beschriftung | Erläuterung | Datentyp |
Input geostatistical layer | Input a geostatistical layer resulting from a Kriging model. | Geostatistical Layer |
Number of output points | Specify how many sample locations to generate. | Long |
Output point feature class | The name of the output feature class. | Feature Class |
Selection criteria (optional) | Methods to densify a sampling network. The Standard error of prediction option will give extra weight to locations where the standard error of prediction is large. The Standard error threshold, Lower quartile threshold, and Upper quartile threshold options are useful when there is a critical threshold value for the variable under study (such as the highest admissible ozone level). The Standard error threshold option will give extra weight to locations whose values are close to the threshold. The Lower quartile threshold option will give extra weight to locations that are least likely to exceed the critical threshold. The Upper quartile threshold option will give extra weight to locations that are most likely to exceed the critical threshold. When the Selection criteria is set to Standard error threshold, Lower quartile threshold, or Upper quartile threshold, the Threshold value parameter will become available, allowing you specify your threshold of interest. The equations for each option are: Standard error of prediction = stderr Standard error threshold = stderr(s)(1 - 2 · abs(prob[Z(s) > threshold] - 0.5)) Lower quartile threshold = (Z0.75(s) - Z0.25(s)) · (prob[Z(s) < threshold]) Upper quartile threshold = (Z0.75(s) - Z0.25(s)) · (prob[Z(s) > threshold])
| String |
Threshold value (optional) | The threshold value used to densify the sampling network. This parameter is only applicable when Standard error threshold, Lower quartile threshold, or Upper quartile threshold selection criteria is used. | Double |
Input weight raster (optional) | A raster used to determine which locations to weight for preference. | Raster Layer |
Input candidate point features (optional) | Sample locations to pick from. | Feature Layer |
Inhibition distance (optional) | Used to prevent any samples being placed within this distance from each other. | Linear Unit |
Mit der Geostatistical Analyst-Lizenz verfügbar.
Zusammenfassung
Uses a predefined geostatistical kriging layer to determine where new monitoring stations should be built. It can also be used to determine which monitoring stations should be removed from an existing network.
Verwendung
The input geostatistical layer must be a kriging layer.
The case might arise where only a single new location is generated when more were requested. This happens when the same new location continues to be selected based on the selection criteria. This can be prevented by specifying a value for the Inhibition distance parameter. Using an inhibition distance is particularly important when using Lower quartile threshold or Upper quartile threshold (in Python, QUARTILE_THRESHOLD or QUARTILE_THRESHOLD_UPPER) as the selection criteria.
To decide which locations have the least influence on the prediction surface you may use the feature class that was used to create the kriging layer for the Input candidate point features parameter. If some monitoring stations need to be decommissioned, the locations with the least influence are good candidates for removal.
Parameter
arcpy.ga.DensifySamplingNetwork(in_geostat_layer, number_output_points, out_feature_class, {selection_criteria}, {threshold}, {in_weight_raster}, {in_candidate_point_features}, {inhibition_distance})| Name | Erläuterung | Datentyp |
in_geostat_layer | Input a geostatistical layer resulting from a Kriging model. | Geostatistical Layer |
number_output_points | Specify how many sample locations to generate. | Long |
out_feature_class | The name of the output feature class. | Feature Class |
selection_criteria (optional) | Methods to densify a sampling network.
The STERR option will give extra weight to locations where the standard error of prediction is large. The STDERR_THRESHOLD, QUARTILE_THRESHOLD, and QUARTILE_THRESHOLD_UPPER options are useful when there is a critical threshold value for the variable under study (such as the highest admissible ozone level). The STDERR_THRESHOLD option will give extra weight to locations whose values are close to the threshold. The QUARTILE_THRESHOLD option will give extra weight to locations that are least likely to exceed the critical threshold. The QUARTILE_THRESHOLD_UPPER option will give extra weight to locations that are most likely to exceed the critical threshold. The equations for each option are: Standard error of prediction = stderr Standard error threshold = stderr(s)(1 - 2 · abs(prob[Z(s) > threshold] - 0.5)) Lower quartile threshold = (Z0.75(s) - Z0.25(s)) · (prob[Z(s) < threshold]) Upper quartile threshold = (Z0.75(s) - Z0.25(s)) · (prob[Z(s) > threshold]) | String |
threshold (optional) | The threshold value used to densify the sampling network. This parameter is only applicable when Standard error threshold, Lower quartile threshold, or Upper quartile threshold selection criteria is used. | Double |
in_weight_raster (optional) | A raster used to determine which locations to weight for preference. | Raster Layer |
in_candidate_point_features (optional) | Sample locations to pick from. | Feature Layer |
inhibition_distance (optional) | Used to prevent any samples being placed within this distance from each other. | Linear Unit |
Codebeispiel
Densify a sampling network based on a predefined geostatistical kriging layer.
import arcpy
arcpy.env.workspace = "C:/gapyexamples/data"
arcpy.DensifySamplingNetwork_ga("C:/gapyexamples/data/Kriging.lyr", 2,
"C:/gapyexamples/output/outDSN")Densify a sampling network based on a predefined geostatistical kriging layer.
# Name: DensifySamplingNetwork_Example_02.py
# Description: Densify a sampling network based on a predefined geostatistical
# kriging layer. It uses, inter alia, the Standard Error of
# Prediction map to determine where new locations are required.
# Requirements: Geostatistical Analyst Extension
# Import system modules
import arcpy
# Set environment settings
arcpy.env.workspace = "C:/gapyexamples/data"
# Set local variables
inLayer = "C:/gapyexamples/data/Kriging.lyr"
numberPoints = 2
outPoints = "C:/gapyexamples/output/outDSN"
# Execute DensifySamplingNetworks
arcpy.DensifySamplingNetwork_ga(inLayer, numberPoints, outPoints)Umgebungen
Sonderfälle
Lizenzinformationen
- Basic: Erfordert Geostatistical Analyst
- Standard: Erfordert Geostatistical Analyst
- Advanced: Erfordert Geostatistical Analyst